Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations7160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory783.3 KiB
Average record size in memory112.0 B

Variable types

Numeric7
Text3
Categorical3
DateTime1

Alerts

is_installed has constant value "OUI"Constant
is_renting has constant value "OUI"Constant
is_returning has constant value "OUI"Constant
code_insee_commune is highly overall correlated with stationcodeHigh correlation
ebike is highly overall correlated with numbikesavailableHigh correlation
mechanical is highly overall correlated with numbikesavailable and 1 other fieldsHigh correlation
numbikesavailable is highly overall correlated with ebike and 2 other fieldsHigh correlation
numdocksavailable is highly overall correlated with mechanical and 1 other fieldsHigh correlation
stationcode is highly overall correlated with code_insee_communeHigh correlation
numdocksavailable has 197 (2.8%) zerosZeros
numbikesavailable has 309 (4.3%) zerosZeros
mechanical has 1550 (21.6%) zerosZeros
ebike has 653 (9.1%) zerosZeros

Reproduction

Analysis started2024-10-20 14:41:36.697205
Analysis finished2024-10-20 14:41:39.664443
Duration2.97 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

stationcode
Real number (ℝ)

HIGH CORRELATION 

Distinct1432
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18714.631
Minimum1001
Maximum92008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:39.708551image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile4010
Q111030.75
median16126.5
Q322012.25
95-th percentile42704
Maximum92008
Range91007
Interquartile range (IQR)10981.5

Descriptive statistics

Standard deviation12172.945
Coefficient of variation (CV)0.65045072
Kurtosis6.5182235
Mean18714.631
Median Absolute Deviation (MAD)5326.5
Skewness1.8252091
Sum1.3399676 × 108
Variance1.4818059 × 108
MonotonicityIncreasing
2024-10-20T16:41:39.769464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 5
 
0.1%
20047 5
 
0.1%
20134 5
 
0.1%
20132 5
 
0.1%
20131 5
 
0.1%
20122 5
 
0.1%
20121 5
 
0.1%
20119 5
 
0.1%
20118 5
 
0.1%
20117 5
 
0.1%
Other values (1422) 7110
99.3%
ValueCountFrequency (%)
1001 5
0.1%
1002 5
0.1%
1003 5
0.1%
1006 5
0.1%
1007 5
0.1%
1008 5
0.1%
1012 5
0.1%
1013 5
0.1%
1014 5
0.1%
1015 5
0.1%
ValueCountFrequency (%)
92008 5
0.1%
92007 5
0.1%
92006 5
0.1%
92005 5
0.1%
92004 5
0.1%
92003 5
0.1%
92002 5
0.1%
92001 5
0.1%
51008 5
0.1%
51007 5
0.1%

name
Text

Distinct1430
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:39.897241image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length50
Median length36
Mean length23.97905
Min length6

Characters and Unicode

Total characters171690
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQuai de l'Horloge - Pont Neuf
2nd rowQuai de l'Horloge - Pont Neuf
3rd rowQuai de l'Horloge - Pont Neuf
4th rowQuai de l'Horloge - Pont Neuf
5th rowQuai de l'Horloge - Pont Neuf
ValueCountFrequency (%)
5565
 
19.6%
de 1405
 
5.0%
place 885
 
3.1%
du 450
 
1.6%
la 370
 
1.3%
gare 325
 
1.1%
porte 265
 
0.9%
jean 260
 
0.9%
des 195
 
0.7%
square 175
 
0.6%
Other values (1857) 18480
65.1%
2024-10-20T16:41:40.102120image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21245
 
12.4%
e 18360
 
10.7%
a 13280
 
7.7%
r 11260
 
6.6%
i 9930
 
5.8%
n 9040
 
5.3%
l 8915
 
5.2%
o 7320
 
4.3%
t 6750
 
3.9%
u 6745
 
3.9%
Other values (70) 58845
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21245
 
12.4%
e 18360
 
10.7%
a 13280
 
7.7%
r 11260
 
6.6%
i 9930
 
5.8%
n 9040
 
5.3%
l 8915
 
5.2%
o 7320
 
4.3%
t 6750
 
3.9%
u 6745
 
3.9%
Other values (70) 58845
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21245
 
12.4%
e 18360
 
10.7%
a 13280
 
7.7%
r 11260
 
6.6%
i 9930
 
5.8%
n 9040
 
5.3%
l 8915
 
5.2%
o 7320
 
4.3%
t 6750
 
3.9%
u 6745
 
3.9%
Other values (70) 58845
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21245
 
12.4%
e 18360
 
10.7%
a 13280
 
7.7%
r 11260
 
6.6%
i 9930
 
5.8%
n 9040
 
5.3%
l 8915
 
5.2%
o 7320
 
4.3%
t 6750
 
3.9%
u 6745
 
3.9%
Other values (70) 58845
34.3%

is_installed
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
OUI
7160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21480
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUI
2nd rowOUI
3rd rowOUI
4th rowOUI
5th rowOUI

Common Values

ValueCountFrequency (%)
OUI 7160
100.0%

Length

2024-10-20T16:41:40.174366image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T16:41:40.214421image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
oui 7160
100.0%

Most occurring characters

ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

capacity
Real number (ℝ)

Distinct63
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.62919
Minimum6
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:40.255989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17
Q123
median29.5
Q337
95-th percentile55
Maximum76
Range70
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.737279
Coefficient of variation (CV)0.3710901
Kurtosis0.67226833
Mean31.62919
Median Absolute Deviation (MAD)6.5
Skewness0.99138926
Sum226465
Variance137.76372
MonotonicityNot monotonic
2024-10-20T16:41:40.314228image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 455
 
6.4%
20 405
 
5.7%
25 335
 
4.7%
28 330
 
4.6%
22 310
 
4.3%
24 305
 
4.3%
26 300
 
4.2%
33 275
 
3.8%
23 255
 
3.6%
32 230
 
3.2%
Other values (53) 3960
55.3%
ValueCountFrequency (%)
6 5
 
0.1%
11 15
 
0.2%
12 30
 
0.4%
13 10
 
0.1%
14 25
 
0.3%
15 70
1.0%
16 120
1.7%
17 140
2.0%
18 160
2.2%
19 165
2.3%
ValueCountFrequency (%)
76 5
 
0.1%
74 5
 
0.1%
71 5
 
0.1%
69 5
 
0.1%
68 20
0.3%
67 25
0.3%
66 25
0.3%
65 30
0.4%
64 10
 
0.1%
63 20
0.3%

numdocksavailable
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.280447
Minimum0
Maximum63
Zeros197
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:40.374103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median17
Q326
95-th percentile41
Maximum63
Range63
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.244887
Coefficient of variation (CV)0.66983522
Kurtosis0.070176017
Mean18.280447
Median Absolute Deviation (MAD)9
Skewness0.6242645
Sum130888
Variance149.93726
MonotonicityNot monotonic
2024-10-20T16:41:40.433149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 271
 
3.8%
18 269
 
3.8%
20 250
 
3.5%
1 246
 
3.4%
2 238
 
3.3%
15 238
 
3.3%
17 237
 
3.3%
21 213
 
3.0%
22 210
 
2.9%
16 209
 
2.9%
Other values (53) 4779
66.7%
ValueCountFrequency (%)
0 197
2.8%
1 246
3.4%
2 238
3.3%
3 204
2.8%
4 199
2.8%
5 157
2.2%
6 185
2.6%
7 172
2.4%
8 199
2.8%
9 194
2.7%
ValueCountFrequency (%)
63 2
 
< 0.1%
62 3
 
< 0.1%
60 3
 
< 0.1%
59 3
 
< 0.1%
58 8
0.1%
57 7
0.1%
56 9
0.1%
55 7
0.1%
54 12
0.2%
53 8
0.1%

numbikesavailable
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.255587
Minimum0
Maximum79
Zeros309
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:40.493864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q318
95-th percentile36
Maximum79
Range79
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.005099
Coefficient of variation (CV)0.97956135
Kurtosis2.6489754
Mean12.255587
Median Absolute Deviation (MAD)6
Skewness1.539758
Sum87750
Variance144.1224
MonotonicityNot monotonic
2024-10-20T16:41:40.556245image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 574
 
8.0%
1 566
 
7.9%
3 494
 
6.9%
4 452
 
6.3%
5 390
 
5.4%
7 326
 
4.6%
6 324
 
4.5%
0 309
 
4.3%
8 248
 
3.5%
9 227
 
3.2%
Other values (64) 3250
45.4%
ValueCountFrequency (%)
0 309
4.3%
1 566
7.9%
2 574
8.0%
3 494
6.9%
4 452
6.3%
5 390
5.4%
6 324
4.5%
7 326
4.6%
8 248
3.5%
9 227
 
3.2%
ValueCountFrequency (%)
79 1
 
< 0.1%
77 1
 
< 0.1%
74 1
 
< 0.1%
73 1
 
< 0.1%
71 1
 
< 0.1%
70 2
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
65 3
< 0.1%
64 3
< 0.1%

mechanical
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.799162
Minimum0
Maximum65
Zeros1550
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:40.614149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312
95-th percentile28
Maximum65
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.8154161
Coefficient of variation (CV)1.2585219
Kurtosis4.1160232
Mean7.799162
Median Absolute Deviation (MAD)4
Skewness1.8718652
Sum55842
Variance96.342393
MonotonicityNot monotonic
2024-10-20T16:41:40.865467image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1550
21.6%
1 935
13.1%
2 620
 
8.7%
3 440
 
6.1%
4 316
 
4.4%
5 274
 
3.8%
6 224
 
3.1%
7 208
 
2.9%
9 193
 
2.7%
8 182
 
2.5%
Other values (55) 2218
31.0%
ValueCountFrequency (%)
0 1550
21.6%
1 935
13.1%
2 620
 
8.7%
3 440
 
6.1%
4 316
 
4.4%
5 274
 
3.8%
6 224
 
3.1%
7 208
 
2.9%
8 182
 
2.5%
9 193
 
2.7%
ValueCountFrequency (%)
65 1
 
< 0.1%
64 1
 
< 0.1%
63 1
 
< 0.1%
62 2
< 0.1%
61 1
 
< 0.1%
60 2
< 0.1%
59 1
 
< 0.1%
58 4
0.1%
57 3
< 0.1%
56 3
< 0.1%

ebike
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4564246
Minimum0
Maximum40
Zeros653
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:40.920691image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile13
Maximum40
Range40
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3448628
Coefficient of variation (CV)0.97496608
Kurtosis7.2390711
Mean4.4564246
Median Absolute Deviation (MAD)2
Skewness2.1753299
Sum31908
Variance18.877833
MonotonicityNot monotonic
2024-10-20T16:41:40.980146image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 1110
15.5%
2 1060
14.8%
3 952
13.3%
4 791
11.0%
0 653
9.1%
5 597
8.3%
6 474
6.6%
7 310
 
4.3%
8 243
 
3.4%
9 184
 
2.6%
Other values (27) 786
11.0%
ValueCountFrequency (%)
0 653
9.1%
1 1110
15.5%
2 1060
14.8%
3 952
13.3%
4 791
11.0%
5 597
8.3%
6 474
6.6%
7 310
 
4.3%
8 243
 
3.4%
9 184
 
2.6%
ValueCountFrequency (%)
40 1
 
< 0.1%
38 1
 
< 0.1%
37 1
 
< 0.1%
33 1
 
< 0.1%
32 3
< 0.1%
31 3
< 0.1%
30 4
0.1%
29 4
0.1%
28 5
0.1%
27 2
 
< 0.1%

is_renting
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
OUI
7160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21480
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUI
2nd rowOUI
3rd rowOUI
4th rowOUI
5th rowOUI

Common Values

ValueCountFrequency (%)
OUI 7160
100.0%

Length

2024-10-20T16:41:41.037353image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T16:41:41.075912image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
oui 7160
100.0%

Most occurring characters

ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

is_returning
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
OUI
7160 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters21480
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOUI
2nd rowOUI
3rd rowOUI
4th rowOUI
5th rowOUI

Common Values

ValueCountFrequency (%)
OUI 7160
100.0%

Length

2024-10-20T16:41:41.119848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-20T16:41:41.159703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
oui 7160
100.0%

Most occurring characters

ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 7160
33.3%
U 7160
33.3%
I 7160
33.3%
Distinct2071
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
Minimum2024-10-17 08:33:05+02:00
Maximum2024-10-17 21:40:07+02:00
2024-10-20T16:41:41.206789image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:41.270139image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1432
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:41.412015image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length37
Median length36
Mean length31.207402
Min length14

Characters and Unicode

Total characters223445
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48.857058739111,2.3417982839439
2nd row48.857058739111,2.3417982839439
3rd row48.857058739111,2.3417982839439
4th row48.857058739111,2.3417982839439
5th row48.857058739111,2.3417982839439
ValueCountFrequency (%)
48.857058739111,2.3417982839439 5
 
0.1%
48.86617395079137,2.3449775576591496 5
 
0.1%
48.8626302,2.3498075 5
 
0.1%
48.86358858111446,2.3477794602513313 5
 
0.1%
48.86362695901683,2.342623919248581 5
 
0.1%
48.86297805972131,2.3414635285735126 5
 
0.1%
48.862382970647,2.3384822532535 5
 
0.1%
48.86364636851945,2.33409583568573 5
 
0.1%
48.863925158727,2.3356226831675 5
 
0.1%
48.866483514509,2.3344334212238 5
 
0.1%
Other values (1422) 7110
99.3%
2024-10-20T16:41:41.628315image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 30080
13.5%
4 25760
11.5%
2 25200
11.3%
3 21350
9.6%
5 17410
7.8%
6 17385
7.8%
7 17370
7.8%
9 16455
7.4%
1 16310
7.3%
0 14645
6.6%
Other values (2) 21480
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 30080
13.5%
4 25760
11.5%
2 25200
11.3%
3 21350
9.6%
5 17410
7.8%
6 17385
7.8%
7 17370
7.8%
9 16455
7.4%
1 16310
7.3%
0 14645
6.6%
Other values (2) 21480
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 30080
13.5%
4 25760
11.5%
2 25200
11.3%
3 21350
9.6%
5 17410
7.8%
6 17385
7.8%
7 17370
7.8%
9 16455
7.4%
1 16310
7.3%
0 14645
6.6%
Other values (2) 21480
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 30080
13.5%
4 25760
11.5%
2 25200
11.3%
3 21350
9.6%
5 17410
7.8%
6 17385
7.8%
7 17370
7.8%
9 16455
7.4%
1 16310
7.3%
0 14645
6.6%
Other values (2) 21480
9.6%
Distinct64
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:41.753456image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length21
Median length5
Mean length7.4036313
Min length5

Characters and Unicode

Total characters53010
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParis
2nd rowParis
3rd rowParis
4th rowParis
5th rowParis
ValueCountFrequency (%)
paris 4820
66.4%
boulogne-billancourt 145
 
2.0%
montreuil 115
 
1.6%
issy-les-moulineaux 110
 
1.5%
pantin 105
 
1.4%
saint-denis 95
 
1.3%
ivry-sur-seine 90
 
1.2%
vitry-sur-seine 75
 
1.0%
aubervilliers 65
 
0.9%
clichy 65
 
0.9%
Other values (57) 1570
 
21.6%
2024-10-20T16:41:41.931012image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 7240
13.7%
s 6640
12.5%
r 6550
12.4%
a 6310
11.9%
P 5025
9.5%
e 3235
 
6.1%
n 2655
 
5.0%
l 2130
 
4.0%
- 1865
 
3.5%
o 1780
 
3.4%
Other values (37) 9580
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7240
13.7%
s 6640
12.5%
r 6550
12.4%
a 6310
11.9%
P 5025
9.5%
e 3235
 
6.1%
n 2655
 
5.0%
l 2130
 
4.0%
- 1865
 
3.5%
o 1780
 
3.4%
Other values (37) 9580
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7240
13.7%
s 6640
12.5%
r 6550
12.4%
a 6310
11.9%
P 5025
9.5%
e 3235
 
6.1%
n 2655
 
5.0%
l 2130
 
4.0%
- 1865
 
3.5%
o 1780
 
3.4%
Other values (37) 9580
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7240
13.7%
s 6640
12.5%
r 6550
12.4%
a 6310
11.9%
P 5025
9.5%
e 3235
 
6.1%
n 2655
 
5.0%
l 2130
 
4.0%
- 1865
 
3.5%
o 1780
 
3.4%
Other values (37) 9580
18.1%

code_insee_commune
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80868.438
Minimum75056
Maximum95018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.1 KiB
2024-10-20T16:41:42.004709image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum75056
5-th percentile75056
Q175056
median75056
Q392040
95-th percentile94041
Maximum95018
Range19962
Interquartile range (IQR)16984

Descriptive statistics

Standard deviation8357.6348
Coefficient of variation (CV)0.10334854
Kurtosis-1.4205869
Mean80868.438
Median Absolute Deviation (MAD)0
Skewness0.75023302
Sum5.7901802 × 108
Variance69850059
MonotonicityNot monotonic
2024-10-20T16:41:42.068362image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75056 4820
67.3%
92012 145
 
2.0%
93048 115
 
1.6%
92040 110
 
1.5%
93055 105
 
1.5%
93066 95
 
1.3%
94041 90
 
1.3%
94081 75
 
1.0%
92049 65
 
0.9%
92024 65
 
0.9%
Other values (54) 1475
 
20.6%
ValueCountFrequency (%)
75056 4820
67.3%
92004 60
 
0.8%
92007 30
 
0.4%
92009 10
 
0.1%
92012 145
 
2.0%
92014 15
 
0.2%
92020 30
 
0.4%
92022 15
 
0.2%
92023 15
 
0.2%
92024 65
 
0.9%
ValueCountFrequency (%)
95018 35
0.5%
94081 75
1.0%
94080 50
0.7%
94076 55
0.8%
94069 15
 
0.2%
94067 30
 
0.4%
94052 20
 
0.3%
94046 30
 
0.4%
94043 25
 
0.3%
94042 5
 
0.1%

Interactions

2024-10-20T16:41:39.172179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:36.880071image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.556517image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.867367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.181223image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.500923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.814242image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.215698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:36.956582image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.599769image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.909302image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.224519image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.541439image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.856367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.263597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.006047image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.642955image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.953575image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.268394image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.585689image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.901687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.310248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.088176image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.684952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.997386image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.311876image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.629482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.982880image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.361415image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.156703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.733111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.043126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.359862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.676984image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.032842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.408272image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.199644image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.775662image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.086810image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.405132image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.719549image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.077592image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.456607image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.510630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:37.819586image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.132395image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.452727image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:38.766216image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-10-20T16:41:39.122874image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-10-20T16:41:42.113501image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
capacitycode_insee_communeebikemechanicalnumbikesavailablenumdocksavailablestationcode
capacity1.000-0.1020.2000.3120.3190.443-0.103
code_insee_commune-0.1021.000-0.049-0.045-0.049-0.0300.819
ebike0.200-0.0491.0000.4460.719-0.437-0.171
mechanical0.312-0.0450.4461.0000.919-0.578-0.244
numbikesavailable0.319-0.0490.7190.9191.000-0.624-0.244
numdocksavailable0.443-0.030-0.437-0.578-0.6241.0000.149
stationcode-0.1030.819-0.171-0.244-0.2440.1491.000

Missing values

2024-10-20T16:41:39.527179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-20T16:41:39.617618image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stationcodenameis_installedcapacitynumdocksavailablenumbikesavailablemechanicalebikeis_rentingis_returningduedatecoordonnees_geonom_arrondissement_communescode_insee_commune
01001Quai de l'Horloge - Pont NeufOUI17215510OUIOUI2024-10-17 15:36:37+02:0048.857058739111,2.3417982839439Paris75056
11001Quai de l'Horloge - Pont NeufOUI1711688OUIOUI2024-10-17 11:30:10+02:0048.857058739111,2.3417982839439Paris75056
21001Quai de l'Horloge - Pont NeufOUI1731477OUIOUI2024-10-17 09:37:07+02:0048.857058739111,2.3417982839439Paris75056
31001Quai de l'Horloge - Pont NeufOUI17215411OUIOUI2024-10-17 18:37:32+02:0048.857058739111,2.3417982839439Paris75056
41001Quai de l'Horloge - Pont NeufOUI1741385OUIOUI2024-10-17 21:38:57+02:0048.857058739111,2.3417982839439Paris75056
51002Victoria - Place du ChateletOUI22317134OUIOUI2024-10-17 11:38:43+02:0048.857992591527,2.3469167947769Paris75056
61002Victoria - Place du ChateletOUI22217152OUIOUI2024-10-17 18:38:45+02:0048.857992591527,2.3469167947769Paris75056
71002Victoria - Place du ChateletOUI22811101OUIOUI2024-10-17 21:39:16+02:0048.857992591527,2.3469167947769Paris75056
81002Victoria - Place du ChateletOUI22515123OUIOUI2024-10-17 15:39:17+02:0048.857992591527,2.3469167947769Paris75056
91002Victoria - Place du ChateletOUI22316133OUIOUI2024-10-17 09:38:30+02:0048.857992591527,2.3469167947769Paris75056
stationcodenameis_installedcapacitynumdocksavailablenumbikesavailablemechanicalebikeis_rentingis_returningduedatecoordonnees_geonom_arrondissement_communescode_insee_commune
715092007Pesaro - PréfectureOUI2215725OUIOUI2024-10-17 18:39:34+02:0048.89577046284005,2.223908342421055Nanterre92050
715192007Pesaro - PréfectureOUI2219312OUIOUI2024-10-17 21:30:58+02:0048.89577046284005,2.223908342421055Nanterre92050
715292007Pesaro - PréfectureOUI22101257OUIOUI2024-10-17 09:38:41+02:0048.89577046284005,2.223908342421055Nanterre92050
715392007Pesaro - PréfectureOUI2213945OUIOUI2024-10-17 11:33:47+02:0048.89577046284005,2.223908342421055Nanterre92050
715492007Pesaro - PréfectureOUI22121046OUIOUI2024-10-17 15:36:27+02:0048.89577046284005,2.223908342421055Nanterre92050
715592008Hôtel de Ville de NanterreOUI382017143OUIOUI2024-10-17 09:35:42+02:0048.892371898250424,2.205548956990242Nanterre92050
715692008Hôtel de Ville de NanterreOUI38261192OUIOUI2024-10-17 15:38:33+02:0048.892371898250424,2.205548956990242Nanterre92050
715792008Hôtel de Ville de NanterreOUI382215114OUIOUI2024-10-17 11:36:38+02:0048.892371898250424,2.205548956990242Nanterre92050
715892008Hôtel de Ville de NanterreOUI38261183OUIOUI2024-10-17 21:37:12+02:0048.892371898250424,2.205548956990242Nanterre92050
715992008Hôtel de Ville de NanterreOUI38261183OUIOUI2024-10-17 18:36:37+02:0048.892371898250424,2.205548956990242Nanterre92050